3-day Virtual Seminar

AI in Clinical Data Management

Overview

This webinar explores AI from the perspective of professionals responsible for managing clinical trial data and the information surrounding it. The session considers areas where generative AI may assist with knowledge-based and administrative work, including summarising suitable information, organising documentation, preparing internal working content, identifying themes for further review and supporting clearer communication across teams.

Attention is also given to the boundaries around AI use. Participants will consider the importance of data quality, source verification, privacy, traceability, human review and working within approved systems and organisational procedures. The session distinguishes between using AI to support productivity and relying on AI for activities that affect formal clinical data processing, validation or trial conclusions.

Why you should Attend

Clinical data can influence important trial conclusions, so speed alone is never enough. AI may help teams work through information more efficiently, but inaccurate suggestions, poor-quality inputs, hidden assumptions or insufficient review can introduce new problems into data-management workflows.

The challenge is knowing where AI can genuinely assist and where established systems, validated processes and qualified human judgement must take priority. This session gives attendees a clearer framework for making those decisions while keeping data quality, traceability and accountability in view.

Areas Covered in the Session

• Understand where AI may support clinical data management teams across information-heavy, documentation and administrative activities
  
• Explore AI-assisted approaches to summarising suitable information, organising content and preparing internal working materials
  
• Consider how AI may help surface patterns, themes or potential areas for further human review without treating AI output as a validated finding
  
• Use clearer prompting techniques to define context, source material, task boundaries and expected outputs
  
• Recognise how poor-quality inputs, hallucinations, missing context and AI-generated assumptions can affect the reliability of outputs
  
• Distinguish productivity support from formal data processing, validation, statistical analysis or other activities requiring approved systems and specialist controls
  
• Apply source verification, traceability and human review when AI contributes to clinical data-related work
  
• Consider privacy, confidentiality, data governance and appropriate handling of clinical or participant information
  
• Assess AI use proportionately according to context, potential impact and the organisation’s established quality and governance requirements
  

Who Will Benefit

• Clinical Data Managers
  
• Clinical Data Management Leads and Managers
  
• Clinical Data Coordinators and Specialists
  
• Clinical Operations Professionals
  
• Clinical Research Associates and Trial Managers
  
• Data Quality and Clinical Quality Professionals
  
• Biostatistics and Data Operations Professionals who collaborate with data-management teams
  
• CRO Clinical Data Management Teams
  
• Managers responsible for digital transformation or AI adoption in clinical research